{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import jieba #中文分词\n",
    "from nltk import FreqDist #词频统计\n",
    "from pyecharts.charts import WordCloud #词云\n",
    "from pyecharts import options as opts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>校园霸凌相关名词</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>暴力</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>残忍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>杀害</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>猥亵</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>扇耳光</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>害怕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>自尊伤害</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>勒索</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>焦虑</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>抵制</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   校园霸凌相关名词\n",
       "0        暴力\n",
       "1        残忍\n",
       "2        杀害\n",
       "3        猥亵\n",
       "4       扇耳光\n",
       "..      ...\n",
       "68       害怕\n",
       "69     自尊伤害\n",
       "70       勒索\n",
       "71       焦虑\n",
       "72       抵制\n",
       "\n",
       "[73 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"D:\\电脑浏然器下载位置/校园霸凌相关名词.xlsx\")\n",
    "df_new = df.drop_duplicates()\n",
    "data_str=str(df_new['校园霸凌相关名词'].values)\n",
    "data_jieba=jieba.lcut(data_str)\n",
    "data_jieba=jieba.lcut(data_str)\n",
    "with open(\"D:\\电脑浏然器下载位置/停用词表.txt\",\"r\",encoding=\"utf-8\") as f:\n",
    "    stop_words=f.read()\n",
    "new_data = []\n",
    "for word in data_jieba:\n",
    "    if word not in stop_words:\n",
    "        new_data.append(word)\n",
    "data_freq = FreqDist(new_data)\n",
    "data_pair = data_freq.most_common()\n",
    "wc = (\n",
    "    WordCloud()\n",
    "    .add(series_name=\"\",data_pair=data_pair,shape=\"triangle\")\n",
    ")\n",
    "wc.render_notebook()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 73 entries, 0 to 72\n",
      "Data columns (total 1 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   校园霸凌相关名词  73 non-null     object\n",
      "dtypes: object(1)\n",
      "memory usage: 712.0+ bytes\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 72 entries, 0 to 72\n",
      "Data columns (total 1 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   校园霸凌相关名词  72 non-null     object\n",
      "dtypes: object(1)\n",
      "memory usage: 1.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "df_new = df.drop_duplicates() #去重\n",
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_str=str(df_new['校园霸凌相关名词'].values)\n",
    "data_str\n",
    "data_jieba=jieba.lcut(data_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[',\n",
       " \"'\",\n",
       " '暴力',\n",
       " \"'\",\n",
       " ' ',\n",
       " \"'\",\n",
       " '残忍',\n",
       " \"'\",\n",
       " ' ',\n",
       " \"'\",\n",
       " '杀害',\n",
       " \"'\",\n",
       " ' ',\n",
       " \"'\",\n",
       " '猥亵',\n",
       " \"'\",\n",
       " ' ',\n",
       " \"'\",\n",
       " '扇',\n",
       " '耳光']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jieba=jieba.lcut(data_str)\n",
    "data_jieba[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除停用词\n",
    "with open(\"D:\\电脑浏然器下载位置/停用词表.txt\",\"r\",encoding=\"utf-8\") as f:\n",
    "    stop_words=f.read()\n",
    "# stop_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['暴力', '残忍', '杀害', '猥亵', '扇', '耳光', '拳打脚踢', '棍棒', '下跪', '围殴']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = []\n",
    "for word in data_jieba:\n",
    "    if word not in stop_words:\n",
    "        new_data.append(word)\n",
    "new_data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'霸凌': 5, '暴力': 2, '残忍': 1, '杀害': 1, '猥亵': 1, '扇': 1, '耳光': 1, '拳打脚踢': 1, '棍棒': 1, '下跪': 1, ...})"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 词频统计\n",
    "data_freq = FreqDist(new_data)\n",
    "data_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('霸凌', 5),\n",
       " ('暴力', 2),\n",
       " ('残忍', 1),\n",
       " ('杀害', 1),\n",
       " ('猥亵', 1),\n",
       " ('扇', 1),\n",
       " ('耳光', 1),\n",
       " ('拳打脚踢', 1),\n",
       " ('棍棒', 1),\n",
       " ('下跪', 1)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pair = data_freq.most_common()\n",
    "wc = (\n",
    "    WordCloud()\n",
    "    .add(series_name=\"\",data_pair=data_pair,shape=\"triangle\")\n",
    ")\n",
    "wc.render_notebook()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc = (\n",
    "    WordCloud()\n",
    "    .add(series_name=\"\",data_pair=data_pair,shape=\"triangle\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min', 'echarts-wordcloud':'https://assets.pyecharts.org/assets/v5/echarts-wordcloud.min'\n",
       "        }\n",
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       "\n",
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       "        require(['echarts', 'echarts-wordcloud'], function(echarts) {\n",
       "                var chart_6f63630f2fa14e95844e05bdfd2666da = echarts.init(\n",
       "                    document.getElementById('6f63630f2fa14e95844e05bdfd2666da'), 'white', {renderer: 'canvas'});\n",
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       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
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       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
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